/**
 * This program is free software, you can redistribute it and/or modify it.
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This file is a part of the CANN Open Software.
 * Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING
 * BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

/*!
 * \file hans_encode_proto.h
 * \brief
 */
#ifndef OP_PROTO_HANS_ENCODE_PROTO_H_
#define OP_PROTO_HANS_ENCODE_PROTO_H_
#include "graph/operator_reg.h"

namespace ge {

/**
 * @brief Losslessly compress the exponent bits of the input tensor.
 *
 * @par Inputs:
 * Two inputs, including:
 * @li input_tensor: A continuous tensor to be compressed. Number of elements must be a multiple of 64.
 * Must be one of the following types: bfloat16, float16, float32.
 * @li pdf: A tensor of type int32, with shape(1, 256). Exponential bit frequency distribution of input tensor.
 *
 * @par Attributes:
 * @li statistic: An optional bool, specifying whether to compute the statistic PDF.
 * True: use the online statistical pdf;
 * False: use the input pdf. Defaults to false.
 * @li reshuff: An optional bool, specifying whether reshuff compression results on continuous memory. Default to false.
 * True: the result of multi-core compression is not reshuffled;
 * False: the result of multi-core compression is reshuffled in memory. Default to false.
 *
 * @attention Constraints:
 * The sum of fixed tensor and var tensor size must be greater than
 * (size(input_tensor) + size(input_tensor) / 64 + 8448 * processCoreDim + 512).
 *
 * @par Outputs:
 * @li pdf: An int32 tensor specifying the exponential bit frequency distribution of the input tensor,
 * valid when statistic is true.
 * @li mantissa: Mantissa output. The type must be the same as input_tensor.
 * @li fixed: Compress output part 1. The type must be the same as input_tensor.
 * @li var: Compress output part 2. The type must be the same as input_tensor.
 *
 * @par Third-party framework compatibility
 * Compatible with the PyTorch operator HansEncode.
 */
REG_OP(HansEncode)
    .INPUT(input_tensor, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT}))
    .INPUT(pdf, TensorType({DT_INT32}))
    .OUTPUT(pdf, TensorType({DT_INT32}))
    .OUTPUT(mantissa, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT}))
    .OUTPUT(fixed, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT}))
    .OUTPUT(var, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT}))
    .ATTR(statistic, Bool, false)
    .ATTR(reshuff, Bool, false)
    .OP_END_FACTORY_REG(HansEncode)

} // namespace ge

#endif // OP_PROTO_HANS_ENCODE_PROTO_H_